Abstract: A system and method for data processing. A method includes sending, by a first system, a plurality of data processing requests to a buffer, wherein the buffer is stored in a second system, wherein the second system is remote from the first system; pulling a portion of the plurality of data processing requests from the buffer; processing the portion of the plurality of data processing requests pulled from the buffer in order to obtain at least one data processing result; and sending the at least one data processing result to the buffer.

Abstract: Systems and methods for visual content processing. A method includes obtaining a subset of media content selected based on outputs of a first machine learning model, wherein the first machine learning model is produced by training a student model using outputs of a teacher model, wherein the outputs of the first machine learning model include a plurality of first predictions for a plurality of portions of the media content; and applying a second machine learning model to the obtained subset of media content, wherein the second machine learning model outputs a plurality of second predictions for respective portions of the plurality of portions, wherein a domain used by the first machine learning model is a subset of a domain used by the second machine learning model.

Abstract: Systems and methods for visual content processing. A method includes applying teacher models to training candidates in order to output instances of a custom object label. The training candidates are selected using a student model based on search configuration parameters. A first set of media content is generated by labeling the training candidates based on the instances of the custom object label output by the teacher models. A custom model is created using the teacher models. The custom model is a machine learning model trained using the first set of media content. A subset of a second set of media content is obtained. The subset of the second set of media content is selected based on outputs of the custom model as applied to the second set of media content. An advanced machine learning model is applied to the obtained subset of the second set of media content.

Abstract: In accordance with disclosed embodiments, there is described a depth camera calibration system which includes: a depth camera to be calibrated; a calibration application to execute upon a mobile device, the calibration application to: (i) determine a precise image size of a calibration image to be displayed to a screen of the mobile device based on a screen size of the mobile device, the calibration image having embedded therein a plurality of objects of a known size, (ii) encode the known size of the objects into an optical machine-readable data representation, and (iii) display the encoded optical machine-readable data representation to the mobile device; in which the depth camera is to read the optical machine-readable data representation displayed by the mobile device to determine the known size of the objects of the calibration image; in which the calibration application is to display the calibration image to the mobile device; and in which an imager of the depth camera is to capture the objects of the c

Abstract: In accordance with disclosed embodiments, there is described a depth camera calibration system which includes: a depth camera to be calibrated; a calibration application to execute upon a mobile device, the calibration application to: (i) determine a precise image size of a calibration image to be displayed to a screen of the mobile device based on a screen size of the mobile device, the calibration image having embedded therein a plurality of objects of a known size, (ii) encode the known size of the objects into an optical machine-readable data representation, and (iii) display the encoded optical machine-readable data representation to the mobile device; in which the depth camera is to read the optical machine-readable data representation displayed by the mobile device to determine the known size of the objects of the calibration image; in which the calibration application is to display the calibration image to the mobile device; and in which an imager of the depth camera is to capture the objects of the c

Abstract: Embodiments of the present disclosure provide techniques and configurations for an optoelectronic three-dimensional object acquisition assembly configured to correct image distortions. In one instance, the assembly may comprise a first device configured to project a light pattern on an object at a determined angle; a second device configured to capture a first image of the object illuminated with the projected light pattern; a third device configured to capture a second image of the object; and a controller coupled to the first, second, and third devices and configured to reconstruct an image of the object from geometric parameters of the object obtained from the first image, and to correct distortions in the reconstructed image caused by a variation of the determined angle of the light pattern projection, based at least in part on the first and second images of the object. Other embodiments may be described and/or claimed.

Abstract: Embodiments of the present disclosure provide techniques and configurations for an optoelectronic three-dimensional object acquisition assembly configured to correct image distortions. In one instance, the assembly may comprise a first device configured to project a light pattern on an object at a determined angle; a second device configured to capture a first image of the object illuminated with the projected light pattern; a third device configured to capture a second image of the object; and a controller coupled to the first, second, and third devices and configured to reconstruct an image of the object from geometric parameters of the object obtained from the first image, and to correct distortions in the reconstructed image caused by a variation of the determined angle of the light pattern projection, based at least in part on the first and second images of the object. Other embodiments may be described and/or claimed.

Abstract: Embodiments of the present disclosure provide techniques and configurations for an optoelectronic three-dimensional object acquisition assembly configured to correct image distortions. In one instance, the assembly may comprise a first device configured to project a light pattern on an object at a determined angle; a second device configured to capture a first image of the object illuminated with the projected light pattern; a third device configured to capture a second image of the object; and a controller coupled to the first, second, and third devices and configured to reconstruct an image of the object from geometric parameters of the object obtained from the first image, and to correct distortions in the reconstructed image caused by a variation of the determined angle of the light pattern projection, based at least in part on the first and second images of the object. Other embodiments may be described and/or claimed.

Abstract: Techniques and configurations for an apparatus for projecting a light pattern on an object are described. In one embodiment, the apparatus may include a laser arrangement configured to generate a laser line, a tiltable micro-electromechanical system (MEMS) mirror configured to tiltably reflect the laser line, and a controller configured to control tilting of the MEMS mirror to enable the reflected laser line to project a light pattern on the object. The controller may be configured to control the MEMS mirror with a tilting frequency that is complementary to an optical power of the laser line, or to control the optical power of the laser line to be complementary to the tilting frequency of the MEMS mirror.

Abstract: A method for acquiring three dimensional coded light data, comprising receiving coded light data through a camera; and converting said received coded light data to representative data, said representative data being reduced in an amount in comparison to said received coded light data, wherein said received coded light data can be reconstructed from said representative data.

Abstract: Embodiments of the present disclosure provide techniques and configurations for an optoelectronic three-dimensional object acquisition assembly configured to correct image distortions. In one instance, the assembly may comprise a first device configured to project a light pattern on an object at a determined angle; a second device configured to capture a first image of the object illuminated with the projected light pattern; a third device configured to capture a second image of the object; and a controller coupled to the first, second, and third devices and configured to reconstruct an image of the object from geometric parameters of the object obtained from the first image, and to correct distortions in the reconstructed image caused by a variation of the determined angle of the light pattern projection, based at least in part on the first and second images of the object. Other embodiments may be described and/or claimed.

Abstract: Embodiments of the present disclosure provide techniques and configurations for an optoelectronic three-dimensional object acquisition assembly configured to correct image distortions. In one instance, the assembly may comprise a first device configured to project a light pattern on an object at a determined angle; a second device configured to capture a first image of the object illuminated with the projected light pattern; a third device configured to capture a second image of the object; and a controller coupled to the first, second, and third devices and configured to reconstruct an image of the object from geometric parameters of the object obtained from the first image, and to correct distortions in the reconstructed image caused by a variation of the determined angle of the light pattern projection, based at least in part on the first and second images of the object. Other embodiments may be described and/or claimed.

Abstract: A projector illuminates an object, within the field of view of a camera, with a sequence of code patterns. The camera captures the illuminated object and provides object images to a decoder to convert the code patterns into code. A transition locator locates discontinuities in the code pattern images. A dequantizer reconstructs a range image from those discontinuities and said code.

Abstract: In a time-multiplexed structured (coded) light camera, the coded light patterns are formed by means of an array of laser sources, and binarizations of the light patterns are performed by computing the dark and bright frames from the code patterns themselves.

Abstract: Embodiments of the present disclosure provide techniques and configurations for an apparatus for projecting a light pattern on an object. In one embodiment, the apparatus may include a laser arrangement configured to generate a laser line, a tiltable micro-electromechanical system (MEMS) mirror configured to tiltably reflect the laser line, and a controller configured to control tilting of the MEMS mirror to enable the reflected laser line to project a light pattern on the object. The controller may be configured to control the MEMS mirror with a tilting frequency that is complementary to an optical power of the laser line, or to control the optical power of the laser line to be complementary to the tilting frequency of the MEMS mirror. Other embodiments may be described and/or claimed.

Abstract: Embodiments of the present disclosure provide techniques and configurations for an optoelectronic three-dimensional object acquisition assembly configured to correct image distortions. In one instance, the assembly may comprise a first device configured to project a light pattern on an object at a determined angle; a second device configured to capture a first image of the object illuminated with the projected light pattern; a third device configured to capture a second image of the object; and a controller coupled to the first, second, and third devices and configured to reconstruct an image of the object from geometric parameters of the object obtained from the first image, and to correct distortions in the reconstructed image caused by a variation of the determined angle of the light pattern projection, based at least in part on the first and second images of the object. Other embodiments may be described and/or claimed.

Abstract: A method for acquiring three dimensional coded light data, comprising receiving coded light data through a camera; and converting said received coded light data to representative data, said representative data being reduced in an amount in comparison to said received coded light data, wherein said received coded light data can be reconstructed from said representative data.

Abstract: A projector illuminates an object, within the field of view of a camera, with a sequence of code patterns. The camera captures the illuminated object and provides object images to a decoder to convert the code patterns into code. A transition locator locates discontinuities in the code pattern images. A dequantizer reconstructs a range image from those discontinuities and said code.

Abstract: In a time-multiplexed structured (coded) light camera, the coded light patterns are formed by means of an array of laser sources, and binarizations of the light patterns are performed by computing the dark and bright frames from the code patterns themselves.

Abstract: An apparatus and system for projecting coded light and for imaging thereof, featuring a micro-mirror for pivoting and for causing each of a plurality of masks to be illuminated sequentially, each mask having a different pattern.